Re: [R] I'm offering $300 for someone who know R-programming to do the assignments for me.
The venom from everyone is to be expected here ayaku. While scientific listservs are a bad place to ask for people to do your homework, they are great for getting advice and soliciting tutors. R is a great program and a lot of fun to learn. Why not put your money to better use and hire a local tutor to help you learn something you can use for many things and many years to come? On Fri, May 8, 2009 at 12:19 AM, ayaku1...@gmail.com ayaku1...@gmail.com wrote: There are six assignments in total. It won't take you long if you were familiar with R. For those who are interested, please send me an email with your profile (your experience with R, how long and how often have you been using it.) I will be paying through paypal. Thanks! __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Adjusting for initial status (intercept) in lme growth models
I read this very brief chapter, and don't see how this would address the issues I raise. Can you provide any further hints? Sorry, I may be missing something obvious. -- DC On Fri, Aug 29, 2008 at 4:07 AM, Dieter Menne [EMAIL PROTECTED]wrote: D Chaws cat.dev.urandom at gmail.com writes: Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that this variability in initial distance is related to growth over time: Looks like a perfect example to use parameter weight=varPower(something) in lme; you could use some power function of the initial distance. See Chapter 5.2 in Pinheiro-Bates. Dieter __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Adjusting for initial status (intercept) in lme growth models
Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that this variability in initial distance is related to growth over time: R# summary(fm1) ... Random effects: Formula: ~1 + I(age - 8) | Subject Structure: General positive-definite, Log-Cholesky parametrization StdDev Corr (Intercept) 1.8866 (Intr) I(age - 8) 0.2264 0.209 Residual1.3100 Now 2 questions: 1. With lme, how can you get a fit of the growth model accounting for the relationship between initial status (intercept) and growth? Some texts call this latent variable regression or something or other, which seems to basically boil down to adding the random effects intercept as a predictor in the growth model. Is this done in lme by simply adding the intercept results from ranef(fm1) to the model? This two-step process seems wrong to me for some reason, perhaps because it seems too simple. Anyone know the proper way to do in lme? 2. In addition, suppose you see that there are significant differences in initial status by Sex: fm2 = lme(distance ~ I(age-8) + Sex, random = ~ 1 + I(age-8) | Subject, data = Orthodont) R# summary(fm2) Fixed effects: distance ~ I(age - 8) + Sex Value Std.Error DF t-value p-value (Intercept) 22.9170.5134 80 44.64 0.000 I(age - 8) 0.6600.0713 809.27 0.000 SexFemale -2.1450.7575 25 -2.83 0.009 Along the lines of question #1, how would you get a growth model adjusting for these Sex differences in initial status? I am looking for something similar to adjusting for baseline differences between Sexes in ANCOVA. I know Lord would not approve, but this is just by way of example... Thanks so much for your help, and this wonderful program Dr. Bates. - DC [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
